DataTalks_Visualization I.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization\n",
    "import dataset from https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>order_id</th>\n",
       "      <th>quantity</th>\n",
       "      <th>item_name</th>\n",
       "      <th>choice_description</th>\n",
       "      <th>item_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Fresh Tomato Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Izze</td>\n",
       "      <td>[Clementine]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Nantucket Nectar</td>\n",
       "      <td>[Apple]</td>\n",
       "      <td>$3.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
       "      <td>$16.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Chicken Bowl</td>\n",
       "      <td>[Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...</td>\n",
       "      <td>$10.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Side of Chips</td>\n",
       "      <td>NaN</td>\n",
       "      <td>$1.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Tomatillo Red Chili Salsa, [Fajita Vegetables...</td>\n",
       "      <td>$11.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Soft Tacos</td>\n",
       "      <td>[Tomatillo Green Chili Salsa, [Pinto Beans, Ch...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>Steak Burrito</td>\n",
       "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Pinto...</td>\n",
       "      <td>$9.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   order_id  quantity                              item_name  \\\n",
       "0         1         1           Chips and Fresh Tomato Salsa   \n",
       "1         1         1                                   Izze   \n",
       "2         1         1                       Nantucket Nectar   \n",
       "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
       "4         2         2                           Chicken Bowl   \n",
       "5         3         1                           Chicken Bowl   \n",
       "6         3         1                          Side of Chips   \n",
       "7         4         1                          Steak Burrito   \n",
       "8         4         1                       Steak Soft Tacos   \n",
       "9         5         1                          Steak Burrito   \n",
       "\n",
       "                                  choice_description item_price  \n",
       "0                                                NaN     $2.39   \n",
       "1                                       [Clementine]     $3.39   \n",
       "2                                            [Apple]     $3.39   \n",
       "3                                                NaN     $2.39   \n",
       "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   \n",
       "5  [Fresh Tomato Salsa (Mild), [Rice, Cheese, Sou...    $10.98   \n",
       "6                                                NaN     $1.69   \n",
       "7  [Tomatillo Red Chili Salsa, [Fajita Vegetables...    $11.75   \n",
       "8  [Tomatillo Green Chili Salsa, [Pinto Beans, Ch...     $9.25   \n",
       "9  [Fresh Tomato Salsa, [Rice, Black Beans, Pinto...     $9.25   "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/justmarkham/DAT8/master/data/chipotle.tsv\", sep = '\\t')\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Create a histogram of the top 5 items bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df[\"item_name\"]\n",
    "order_count = Counter(x) # dict\n",
    "new_df = pd.DataFrame.from_dict(order_count, orient = \"index\") #convert dict to dataframe\n",
    "new_df = new_df[0].sort_values(ascending = True)[45:50]\n",
    "\n",
    "new_df.plot(kind = \"bar\")\n",
    "\n",
    "plt.xlabel(\"Items\")\n",
    "plt.ylabel(\"Number of Time Order\")\n",
    "plt.title(\"Most ordered Chipotle\\'s Items\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create a scatterplot with the number of items orderered per order price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 36.7178857951459)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df[\"item_price\"] = [float(value[1:-1]) for value in df.item_price]\n",
    "orders = df.groupby(\"order_id\").sum()\n",
    "\n",
    "plt.scatter(x = orders.item_price, y = orders.quantity, s = 50, c = \"blue\")\n",
    "\n",
    "plt.xlabel(\"Order Price\")\n",
    "plt.ylabel(\"Item Ordered\")\n",
    "plt.title(\"Number of items ordered per order price\")\n",
    "\n",
    "plt.ylim(0)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}